Techniques for howling detection

ABSTRACT

The application describes techniques for howling detection. A howling detector is described that is configured to receive an input signal and to determine measure of the linearity of a logarithmic representation of the energy of the input signal. In some examples, this triggers gain adjustment (e.g. of a noise control unit) and, in some further examples, the amount of the gain adjustment may be based on an estimation of the maximum stable gain of a noise control unit.

TECHNICAL FIELD

Embodiments described herein relate to techniques for active noisecancellation. In particular, embodiments described herein relate totechniques for suppressing an acoustic feedback signal.

BACKGROUND

Many devices include loudspeakers, which are used to play sounds to auser of the device, based on an input signal. For example, the inputsignal may be derived from a signal that has been received by the deviceover a communications link, in the case of a phone call or the like, ormay be derived from stored data, in the case of music or speechplayback.

As wireless communication devices, Mp3 players and other devices foraudio playback move even further into everyday use, features like noisecancellation become more important to help ensure higher-quality audioplayback and phone calls.

Noise cancellation embraces a number of different approaches toeliminating unwanted noise in order to enhance the listening experienceof a user. Active noise cancellation (ANC) or noise control refers to amethod of reducing noise by the addition of anti-noise—i.e. a phaseinverted noise signal—which destructively interferes with the noise.This is generally achieved by using a reference microphone to senseenvironmental or ambient noise and by deriving an anti-noise signal thatis emitted by a speaker in order to cancel or at least control thenoise. As will be appreciated by those skilled in the art active noisecontrol can be achieved with analogue filters or digital filters, and isgenerally differentiated by architecture: feed-forward cancellation,feedback cancellation or hybrid cancellation.

FIG. 1 provides a simplified illustration of a feedforward ANC system.As illustrated in FIG. 1, a reference microphone 10 detects incidentambient sounds—or noise—and generates an input signal x(n) for an ANCcircuit 20. The ANC circuit 20 processes the signal in order to derive acontrol signal y(n) which is passed to the loudspeaker transducer 30 andis emitted by the loudspeaker 30 as anti-noise. Thus, the ANC circuitmay be considered to comprise a filter having a transfer function H_(nc)which inversely models the noise signal for generating the requiredcontrol sound signal. An error microphone (not shown) is typicallyprovided to measure the error between the noise signal and theanti-noise signal in order that the transfer function H_(nc) or therespective gain of the ANC circuit may be adapted.

As illustrated in FIG. 2, it will be appreciated that the anti-noisesignal will not only propagate on a path towards a user's ear H_(de)(where d denotes the driver and e denotes the ear), but may alsopropagate on a leakage path, or feedback path H_(dm) (where d denotesthe driver/loudspeaker and m denotes the microphone), towards thereference microphone. This is known as acoustic feedback and results ina corrupted reference signal u(n). Thus, the reference signal willadditionally contain the acoustic feedback signal that is sensed by thereference microphone. When an acoustic control system has a feedbackpath the leakage often causes unstable behaviour called howling whichresults in an audible feedback tone.

It will therefore be appreciated that the stability of a noise controlsystem will be significantly influenced by the feedback signal and willdepend on the transfer characteristics of an acoustic feedback pathH_(dm) between the speaker and the reference microphone. A similarproblem can arise when the speech captured by the voice microphone leaksto the speaker (driver). The problem of acoustic feedback isparticularly an issue in the case of a mobile communication device, suchas a mobile phone, due to the close proximity between the referencemicrophone and the speaker.

The frequency of the feedback tone depends on Hdm in conjunction withHnc. Since both Hdm and Hnc can change, the tone frequency can changerespectively across a wide range of frequencies. The level of thefeedback tone may rise quickly as the energy of the reference signalrises exponentially. Thus, the feedback tone is unpleasant andpotentially damaging to the ear. There is therefore a need to try tomanage and/or suppress the occurrence of howling.

A number of techniques have been proposed that seek to detect theoccurrence of howling in order to enable the gain of the circuit to beautomatically adjusted. However, the previously considered techniquessuffer from a number of disadvantages, including high latency, the needfor relatively complex frequency domain processing and problemsassociated with the occurrence of false positive detection of howling.For example, a previously proposed method involves a maximum peakdetection method which involves performing a linear search for asustained peak in the energy of the signal across multiple consecutiveframes. As a consequence of the need to locate and track several framesof the signal, the system introduces a latency between the initialoccurrence of howling and the detection of howling. Consequently, anysubsequent measures taken to mitigate the feedback tone take place aftera certain delay, and potentially after the sound has risen to an audiblelevel.

The present examples are concerned with techniques for detectinghowling, in particular to howling detection techniques which alleviateone or more of the problems associated with previously proposed howlingdetection methods.

According to one example of a first aspect of the present inventionthere is provided a howling detector (or howling detection unit)configured to receive an input signal and to determine a measure of thelinearity of a logarithmic representation of the energy of the inputsignal. As will be explained below, according to one or more examplesherein the measure of linearity (or linearity metric) of the logarithmof the energy may constitute an indication that howling is likely,imminent, or occurring and therefore the howling detector of someexamples herein is configured to detect the presence of howling. In someexamples, the input signal may comprise a feedback signal.

According to one or more examples, the howling detector may beconfigured to issue a command to a gain adjuster if the measure oflinearity exceeds a predetermined threshold. The command issued to thegain adjuster may comprise instructions that, when executed by the gainadjuster, cause the gain adjuster to reduce the gain. The command issuedto the gain adjuster may comprise instructions that, when executed bythe gain adjuster, cause the gain adjuster to adjust the gain, forexample by a fixed amount. In this way, once the howling detectordetects howling (or imminent or likely howling), it may issue a commandto a gain adjuster to suppress the howling. As the howling may beconfigured to automatically issue the command to the gain adjuster thehowling detector is therefore configured to actively supress, or cancel,the effects of howling. It will be appreciated that, in some examples,the measure of linearity exceeding a predetermined threshold may bedetermined by a parameter, or phasemeters, falling below a predeterminedthreshold. In other words, a parameter (examples will be provided belowand include a residual value or a derivative) falling below a thresholdmay indicate that the measure of linearity of the logarithm of energyexceed a threshold. In other words, certain parameters may decrease themore linear the logarithm of the energy becomes.

According to one or more examples, the howling detector may beconfigured to issue a command to a gain adjuster if the measure oflinearity falls below a predetermined threshold, depending on themeasure of linearity as will be explained below.

Determining a measure of the linearity of the logarithmic representationof the energy of the input signal may, in some examples, comprisecomputing a difference between the logarithm of the energy of the signaland a trend line, the trend line being a straight line representation ofthe logarithm of the energy of the signal. In this way, the measure oflinearity may be the difference between the logarithm of the signalenergy and a straight line representation of the logarithm of the signalenergy, and therefore when the logarithm of the energy tends towardslinearity this difference will become smaller. This difference may bemeasured by the residuals of the trend line.

Accordingly, in one example, the howling detector may be configured toissue command to a gain adjuster if the average of the residuals of thetrend line are below a threshold. In this way, when the residuals of thetrend line are below a threshold this may be an indication that thelogarithm of the energy tends sufficiently towards linearity so as toinfer not just the presence of howling, but when howling is likely orimminent (depending on the threshold value). Issuing the command to thegain adjuster when the residuals fall below a threshold means that thehowling detector may instruct the gain adjuster to reduce the gain whenhowling is likely or imminent, and therefore, in some examples, beforehowling has actually occurred.

According to one or more examples, coefficients of the trend line arederived by fitting a line to the logarithmic representation of theenergy of the input signal. Fitting a line to a logarithmicrepresentation of the energy of the input signal may comprise performinga least squares computation which minimises a difference between thetrend line and the logarithmic representation of the energy of the inputsignal.

The measure of linearity may be determined according to a goodness offit measure of the computed trend line to the logarithm of the energy ofthe input signal over P samples. In one example, the goodness of fitmeasure r(n) may be represented by:

${{r(n)} = \sqrt{\sum\limits_{i = 0}^{P - 1}\left( {{y\left( {n - i} \right)} - \left( {{{\hat{\beta}}_{0}(n)} + {\left( {P - i} \right) \cdot {{\hat{\beta}}_{1}(n)}}} \right)} \right)^{2}}},$

where y(n) is the log energy of the input signal, {circumflex over(β)}₀(n) is the bias of the trend line and {circumflex over (β)}₁(n) isthe slope of the trend line.

The howling detector may be configured to issue the command to the gainadjuster when it is detected that the slope {circumflex over (β)}₁(n) isconverging to the value

$\frac{1}{P - 1}.$

In this example, convergence of the slope may sufficiently indicate thatthe logarithm of the signal energy is linear, or sufficiently tendingtowards linearity. Convergence of the slope may be measured by the slopebeing within a confidence interval and, accordingly, detecting that theslope is converging may comprise determining whether the slope lieswithin a confidence interval, for example the interval:

${{\left( \frac{1}{P - 1} \right) \cdot \left( {1 - \alpha} \right)} \leq {\overset{\_}{\beta}}_{1} \leq {\left( \frac{1}{P - 1} \right) \cdot \left( {1 + \alpha} \right)}},$

where α is a parameter.

The howling detector may be configured to determine a derivative of thelogarithm of the energy. The howling detector may be configured to issuethe command to the gain adjuster when the derivative of the logarithm ofthe energy tends towards zero or is less than a threshold.

According to an example of a second aspect there is provided circuitryfor receiving an input signal and determining a measure of the linearityof the logarithmic representation of the energy of the input signal. Theinput signal may comprise a feedback signal.

The circuitry may, in some examples, comprise a gain adjuster configuredto reduce the gain if the measure of linearity exceeds a certainthreshold. In these examples, the circuitry may be configured to controlthe gain adjuster to reduce the gain if the measure of linearity exceedsa predetermined threshold. The gain adjuster may be configured to adjustthe gain by an amount that related to a difference between a measure ofthe linearity of the plot of the energy of the input signal in the logdomain and a target linearity metric. For example, the gain adjuster maybe configured to adjust the gain by a fixed amount, or by an amountwhich is proportional to the slope of the trend line (as describedabove).

The circuitry may comprise a filterbank configured to split the inputsignal into a plurality of frequency bands, wherein the circuitry isoperable to determine a measure of the linearity of the logarithmicrepresentation of the energy for each frequency band.

The circuitry may comprise the howling detector as described above. Inthis example, the howling detector may be configured to issue a commandto the gain adjuster if the measure of linearity exceeds a predeterminedthreshold.

The circuitry may comprise a noise control module, or a noise controlsystem (e.g. a noise reduction system) such as active noise controlmodule or active noise control system. The circuitry may compriseprocessing circuitry for one of the aforementioned modules or systems.

According to one example of a third aspect there is provided a methodcomprising: receiving an input signal, determining a measure of thelinearity of the logarithmic representation of the energy of the inputsignal.

The method may further comprise issuing a command to a gain adjuster ifthe measure of the linearity exceeds a predetermined threshold.

Determining a measure of the linearity of the logarithmic representationof the energy of the input signal may comprise computing a differencebetween the logarithm of the energy of the signal and a trend line, thetrend line being a straight line representation of the logarithm of theenergy of the signal, and the method may further comprise issuing thecommand to the gain adjuster if the average of the residuals of thetrend line are below a threshold.

Determining a measure of the linearity of the logarithmic representationof the energy of the input signal may comprise calculating a goodness offit measure of a computed trend line being a straight linerepresentation of the logarithm of the energy of the signal to thelogarithm of the energy of the input signal over P samples, wherein thegoodness of fit measure r(n) is represented by:

${{r(n)} = \sqrt{\sum\limits_{i = 0}^{P - 1}\left( {{y\left( {n - i} \right)} - \left( {{{\overset{\hat{}}{\beta}}_{0}(n)} + {\left( {P - i} \right) \cdot {{\overset{\hat{}}{\beta}}_{1}(n)}}} \right)} \right)^{2}}},$

where y(n) is the log energy of the input signal, {circumflex over(β)}₀(n) is the bias of the trend line and {circumflex over (β)}₁(n) isthe slope of the trend line, and the method may further comprise issuingthe command when it is detected that the slope {circumflex over (β)}₁(n)is converging to the value

$\frac{1}{P - 1}.$

Determining a measure of the linearity of the logarithmic representationof the energy of the input signal may comprise determining a derivativeof the logarithm of the energy, and the method may further compriseissuing the command to the gain adjuster when the derivative of thelogarithm of the energy tends towards zero or is less than a threshold.

According to an example of a fourth aspect there is provided aprocessing module for a noise control circuit, the processing modulecomprising:

-   -   a howling detector configured to receive an input signal and to        determine a linearity metric based on the input signal, the        linearity metric comprising a measure of the linearity of a        logarithmic representation of the energy of the input signal;        and    -   a gain adjuster configured to adjust the gain of a noise control        unit.

The linearity metric—or log-linearity metric—may be derived by computinga difference between the logarithm of the energy of the signal and atrend line, the trend line being is a straight line representation ofthe logarithm of the energy of the signal. Coefficients of the trendline may be derived by a fitting a line to the logarithmicrepresentation of the energy of the input signal. The process of fittinga line to the logarithmic representation of the energy of the inputsignal may comprise performing a least squares computation whichminimises a difference between the trend line and the logarithmicrepresentation of the energy of the input signal.

According to one or more examples the linearity metric is determinedaccording to a goodness of fit measure of the computed trend line to thelogarithm of the energy of the input signal over P samples. The goodnessof fit measure r(n) may be represented by:

${r(n)} = \sqrt{\sum\limits_{i = 0}^{P - 1}\left( {{y\left( {n - i} \right)} - \left( {{{\hat{\beta}}_{0}(n)} + {\left( {P - i} \right) \cdot {{\hat{\beta}}_{1}(n)}}} \right)} \right)^{2}}$

where y(n) is the log energy of the input signal, {circumflex over(β)}₀(n) is the bias of the trend line and {circumflex over (β)}₁(n) isthe slope of the trend line.

According to one or more examples the howling detection unit may befurther configured to issue a command to the gain adjuster in order toreduce the gain if the linearity metric exceeds a predeterminedthreshold. The gain adjuster may be configured to adjust the gain by afixed amount or by an amount which is proportional to the slope of thetrend line.

According to one or more examples the howling detection unit may befurther configured to estimate a maximum stable gain of the noisecontrol unit. Te maximum stable gain is proportional to the slope of thetrend line.

The howling detection unit may be configured to determine, based on thedetermined linearity metric, if howling is likely or imminent.

According to a further aspect there is provided an audio processingsystem comprising a processing module according to the fourth aspect.The audio processing system may further comprise a noise control unitfor generating a noise control signal based on a reference input signalwhich represents a sound detected by a reference microphone. Theprocessing module may be connected to a speaker and wherein the speakergenerates an anti-noise signal based on the noise control signal inorder to cancel or at least reduce the noise detected by a referencemicrophone.

According to one example the howling detector may be provided inparallel with the noise control unit.

According to one example the audio processing system may furthercomprise a filter configured to filter out one or more frequencies orfrequency bands of the input signal. The audio processing system maycomprise a filterbank configured to split the input signal into aplurality of frequency bands, wherein the howling detector is operableto determine a linearity metric for each frequency band.

According to at least one example of a sixth aspect, there is provided aprocessing circuit for a noise control module comprising: a gainadjustment mechanism configured to adjust the gain of a noise controlcircuit if a plot of the energy of an input signal in the log domainbecomes linear or tends towards linearity.

The gain of the noise control circuit may be adjusted by an amount thatrelated to an amount which is proportional to the slope of the trendline.

The processing module may be provided in the form of a monolithicintegrated circuit.

According to an example of a further aspect there is provided a devicecomprising a processing module according to the fourth aspect or aprocessing circuit according to the sixth aspect, wherein the devicecomprises a mobile telephone, headphone, acoustic noise cancellingheadphones, a smart watch, an audio player, a video player, a mobilecomputing platform, a games device, a remote controller device, a toy, amachine, or a home automation controller, a domestic appliance or otherportable device.

According to a seventh aspect there is provided a method of processingan audio signal comprising:

-   -   determining a linearity metric based on an input signal, the        linearity metric comprising a measure of the linearity of a        logarithmic representation of the energy of the input signal;        and    -   adjusting the gain of the noise control unit if the linearity        metric exceeds a predetermined threshold.

The step of determining a linearity metric may comprise computing adifference between the logarithm of the energy of the signal and a trendline, the trend line being is a straight line representation of thelogarithm of the energy of the signal.

Aspects of the invention therefore relate to the detection of occurring,imminent, or likely to be imminent, howling and its suppression. Forexample, some examples relate on the monitoring of parameters that, byexceeding or falling below a pre-determined threshold will indicate alikely to be imminent, or imminent, occurrence of howling. When thisoccurs a signal may be sent to adjust the gain so that howling can beactively suppressed before howling actually occurs. In this way, someaspects of the invention may be used as part of an active noisereduction, or control, system, that will positively act to suppresshowling before it has occurred. Some aspects of the invention maytherefore be used in conjunction with an active noise reduction, orcontrol system. However, in, further examples, some aspects may be usedas a standalone apparatus to monitor and/or detect the presence ofhowling.

BRIEF DESCRIPTION OF DRAWINGS

For a better understanding of the present invention and to show how thesame may be carried into effect, reference will now be made by way ofexample to the accompanying drawings in which:

FIG. 1 provides a simplified illustration of a feedforward ANC system;

FIG. 2 provides a further illustration of a feedforward ANC system;

FIG. 3 illustrates a wireless communication device implementing a noisecontrol circuit in accordance with a first example;

FIG. 4 is a block diagram of the audio integrated circuit provided inthe wireless communication device depicted in FIG. 3;

FIG. 5 illustrates a first example of a processing module;

FIG. 6 illustrates a further example a processing module according tothe present aspects implemented within a noise control module 140;

FIGS. 7a and 7b show examples of a line fitting method;

FIG. 8 illustrates a further example of a howling detection circuitimplemented within an audio processing circuit;

FIG. 9 shows first and second plots to illustrate the increasing slopein the energy or of an input signal;

FIG. 10 shows a plurality of data samples plotted on a graphicalrepresentation of the amplitude of an input signal, as well as a fittedline;

FIG. 11 shows the log energy of the signal output for 3 different cases;

FIG. 12 shows several plots to illustrate the signals used by theproposed method to detect howling;

FIG. 13 illustrates a howling detection process according to oneexample; and

FIG. 14 illustrates a system incorporating multiband howling detection.

DETAILED DESCRIPTION

The description below sets forth examples according to the presentdisclosure. Further example embodiments and implementations will beapparent to those having ordinary skill in the art. Further, thosehaving ordinary skill in the art will recognize that various equivalenttechniques may be applied in lieu of, or in conjunction with, theexamples discussed below, and all such equivalents should be deemed asbeing encompassed by the present disclosure.

The methods described herein can be implemented in a wide range ofdevices and systems. However, for ease of explanation of one example, anillustrative example will be described, in which the implementationoccurs in a mobile communication device such as a mobile phone.

FIG. 3 illustrates a wireless communication device 100 implementing anoise control unit in accordance with any of the present aspects. Thewireless communication device 100 is shown in proximity with a user'sear 50.

The wireless communication device comprises a transducer, such as aspeaker 130, which is configured to reproduce distance sounds, such asspeech, received by the wireless communication device along with otherlocal audio events such as ringtones, stored audio program material, andother audio effects including a noise control signal. A referencemicrophone 110 is provided for sensing ambient acoustic events. Thewireless communication device further comprises a near-speech microphone150 which is provided in proximity to a user's mouth to sense sounds,such as speech, generated by the user.

A circuit 125 within the wireless communication device comprises anaudio CODEC integrated circuit (IC) 180 that receives the signals fromthe reference microphone, the near-speech microphone 150 and interfaceswith the speaker and other integrated circuits such as a radio frequency(RF) integrated circuit 12 having a wireless telephone transceiver.

FIG. 4 is a block diagram of the audio integrated circuit 180 providedin the wireless communication device depicted in FIG. 3 and illustratesselected units of the integrated circuit. Specifically, the integratedcircuit receives an input signal from reference microphone 110 andincludes an analog-to-digital converter (ADC) 135 a for generating adigital representation of the input signal x(n) which is passed to anoise control unit 140 according to an example of the present aspects,wherein the noise control circuit is configured to generate a controlsignal u(n).

The audio integrated circuit comprises a further ADC 135 b forgenerating a digital representation of the signal generated by thenear-speech microphone 150. Combiner 136 may combine audio signalsincluding the noise control signal u(n) (which by convention may havethe same polarity as the noise in the reference microphone signal andwill therefore be subtracted by the combiner 136), a portion of thenear-speech microphone signal to allow a user of the wirelesscommunication device to hear his or her own voice, in addition todownlink speech communication which is received from the radio frequencyintegrated circuit 115. The digital-to-analogue converter 137 receivesthe output of the combiner 136, amplifies the output of the DAC (Notshown) and passes the resultant signal to the speaker 130.

From consideration of FIG. 3 it will be appreciated that a feedback pathH_(dm) arises between the speaker 130 and the reference microphone 110.

FIG. 5 shows a processing module (howling detection system) 200comprising a howling detector (HD) 150 and a gain adjuster 160 (e.g.amplifier) configured to control the gain of a noise control systemcomprising a noise control unit 120. The howling detector is configuredto determine a linearity metric, or log-linearity metric, which isindicative of a measure of the linearity of the variation (over Psamples) in the energy of an input signal u(n) in the logarithm domain.Thus, one or more of the present examples can be considered to rely upona consideration of the change in the energy of the signal u(n) in thelog domain.

According to one or more examples the linearity metric may be derived bycomputing a difference between the logarithm of the energy of the signalu(n) and a line—referred to herein as a trend line—which is a straightline representation of the logarithm of the energy of the signal u(n).It will be appreciated that the trend line may be derived by fitting aline of best fit to the log-energy data derived from the observed signalsamples.

According to one or more examples the linearity metric may be determinedaccording to a goodness of fit measure of the computed trend line to thelogarithm of the energy of the input signal u(n) over P samples. Thegoodness of fit measure may be computed, for example, as the leastsquare L²-norm of the residuals r(n) of the trend line fitted to theobserved energy data points. Thus, r(n) may be represented by:

$\begin{matrix}{{r(n)} = \sqrt{\sum\limits_{i = 0}^{P - 1}\left( {{y\left( {n - i} \right)} - \left( {{{\hat{\beta}}_{0}(n)} + {\left( {P - i} \right) \cdot {{\hat{\beta}}_{1}(n)}}} \right)} \right)^{2}}} & {{Eq}.\mspace{14mu}(1)}\end{matrix}$

Where y(n) is the log energy of the signal u(n), {circumflex over(β)}₀(n) is the bias of the trend line and {circumflex over (β)}₁(n) isthe slope of the trend line. Thus, equation 1 can be understood torepresent the least squared difference between the logarithm of theenergy of the signal y(n) and a straight line representation of thelogarithm of the energy of the signal u(n). In other words, thelinearity metric that is determined by the howling detector 150according to the present examples may be considered to be r(n).

FIGS. 7a and 7b show examples of a line fitting method. In both plotsthe solid line represents the output log energy y(n) and the dashed lineis the computed trend line, or fitted line, having line equationcoefficients {circumflex over (β)}(n). In FIG. 7a a linearity metricderived on the basis of the computed trend line will be relatively low,indicating high residual values r, because the line doesn't closelyrepresent y(n). However, in FIG. 7b a linearity metric derived on thebasis of the computed trend line will be relative high, indicating lowresidual values r, because the signal y(n) is closely represented by thefitted line. Thus, it will be appreciated that the linearity metric orgoodness of fit measure can be used as an indicator of howling bydetecting when the linearity metric exceeds a threshold value (in otherwords when the residual values r(n) being below a given threshold). Itwill also be appreciated that as the logarithm of the signal energytends towards linearity its derivative will tend towards zero, thegradient or slope of straight lines being zero. Therefore, thederivative of the logarithm of the energy may be used as an indicator ofhowling by detecting when the derivative is below a given threshold(indicating that the line that is the logarithm of the energy is tendingtowards a zero gradient).

According to one or more examples, if the linearity metric determined bythe howling detector 150 exceeds a predetermined threshold this is anindication that howling is occurring.

According to one or more examples the howling detection unit 150 isconfigured to issue a command to the gain adjuster 160 to reduce thegain of the noise control unit. According to one or more examples thegain is reduced by a fixed amount in order to effectively reduce or turnoff the noise control functionality. Thus, the howling detection circuitmay be configured to cause the gain adjuster to drop the gain by a fixedamount that is pre-selected in accordance with the operating conditionsof the overall system. For example, the howling detection circuit may beconfigured to cause the gain adjuster to drop the gain by between 25 and40 dB, preferably by 35 dB in order to immediately kill the tone,interrupt the function of the noise control unit and alleviate any otheraudio distortion.

FIG. 6 illustrates a possible implementation of a howling detectioncircuit, comprising a howling detector 150 and a gain adjuster 160within a noise control module 140. The noise control module or noisecontrol circuit may be implemented within a monolithic integratedcircuit such as the CODEC 180 illustrated in FIG. 4, or may beimplemented within any other audio processing circuit which incorporatesa noise control unit for controlling or cancelling noise. It will alsobe appreciated that the noise control module may be provided in avariety of different devices, not just within a mobile phone.

The noise control module 140 comprises a noise control unit 120 whichreceives, e.g. via an analogue-to-digital converter (not shown), aninput signal x(n) derived from the output signal of a referencemicrophone 110. The input signal represents ambient noise and othersounds including a feedback signal h_(dm) detected by the referencemicrophone. The noise control unit 120 is operable to generate a controlsignal u(n) based on the input signal x(n). The control signal comprisesan anti-noise signal—in other words a signal representing the noise butwith inverted phase (antiphase)—that is emitted by a speaker 130 of thedevice in which the noise control circuit is implemented, in order tocancel or at least control or mitigate the level of the noise that isheard by a user.

In this example the control signal forms the input signal u(n) for thehowling detection system 200.

FIG. 8 illustrates a further example in which a howling detectioncircuit 200 may be provided in conjunction with an automatic gainadjustment (AGA) circuit 170 which is operable, based on an input signalderived from an error microphone of the noise control system, to trackthe energy of the input signal over multiple frames and to makeadjustments to the gain of the ANC circuit via the gain adjuster 160.Thus, the gain G may be primarily controlled by the AGA system in orderto minimise the residual noise at the speaker 130. The howling detectioncircuit may be configured to take control of the gain adjuster 160 ifhowling is detected. If howling occurs, the howling detection circuit isconfigured to cause the gain adjuster to drop the gain by around 25 to40 dB, in order to interrupt the operation of the noise control unit 120and bring a stop to the audio distortion.

One or more examples described herein may advantageously allow howlingdetection based on a single frame (e.g. 5 msec). Thus, the presentexamples potentially benefit from a faster detection of howling thanpreviously proposed techniques which require computations to take placeover multiple frames. Furthermore, examples of the present aspects areadvantageously performed purely in the time domain, circumventing theneed to transform the signal to frequency domain, thereby lowering thecomplexity of the system and easing the memory capacity requirements incomparison to techniques which require frequency domain processing.

A theoretical background to the present examples will now be discussedwith reference to FIG. 9.

When the howling occurs, an exponentially rising sinusoid appears in thesignal of interest u(n). The increasing slope in the energy or amplitudeof u(n), which is illustrated graphically by the first plot shown inFIG. 9, is clearly exponentially dependent on the gain. Thus, it will beappreciated that if the logarithm of the energy of the signal iscomputed, the dependence will become linear, as illustrated in thesecond plot shown in FIG. 9. In other words, when howling is present thelog energy increases linearly.

Examples described herein are based on monitoring for an exponentialincrease in the amplitude of the signal, which may be readily recognisedfrom a linear increase in the log-domain (dB), in order to try to detector predict the occurrence of howling.

The output log energy y(n) may be computed using a smoothing of theinstant energy of the input signal u(n). Thus:

y(n)=log(a(n)), where a(n)=λ_(y) ·a(n−1)+(1−λ_(y))·u ²(n)   Eq. (2)

The energy a(n) is therefore proportional to the square of the sample.

If we define a straight line where {circumflex over (β)}₀(n) is the biasof the line and {circumflex over (β)}₁(n) is the slope of the line, theline equation term in equation 1 can be written in the vector format as:

{circumflex over (β)}₀+(P−i)·{circumflex over (β)}₁=[{circumflex over(β)}₀(n), {circumflex over (β)}₁(n)]^(T)·[1, P−i]={circumflex over(β)}(n)·[1, P−i]  Eq. (3)

Where T indicate matrix transpose operation.

The line equation coefficients {circumflex over (β)}(n) can be computedby minimising a cost function between observed samples and fitted lineequation. This can be done for example by solving a least squaresproblem which tries to find the line {circumflex over (β)}(n) thatminimises the Euclidean distance to the observed data y(n).

$\begin{matrix}{{\overset{\hat{}}{\beta}(n)} = {{\arg\;{\min\limits_{\beta}\left( {r(n)}^{2} \right)}} = {\left( {X^{T}X} \right)^{- 1}X^{T}{y(n)}}}} & {{Eq}.\mspace{14mu}(4)}\end{matrix}$

Where:

${X = \begin{bmatrix}1 & 1 \\1 & 2 \\\vdots & \vdots \\1 & P\end{bmatrix}},{{y(n)} = \begin{bmatrix}{y\left( {n - P + 1} \right)} \\{y\left( {n - P + 2} \right)} \\\vdots \\{y(n)}\end{bmatrix}}$

In order to make this approach robust to different energy levels of theincoming signal u(n), the signal y(n) may be normalised such that it isbetween 0 and 1.

$\begin{matrix}{{y^{\prime}(n)} = \frac{{y(n)} - {\min\left( {y(n)} \right)}}{{\max\left( {y(n)} \right)} - {\min\left( {y(n)} \right)}}} & {{Eq}.\mspace{14mu}(5)}\end{matrix}$

Due to this normalisation, the slope {circumflex over (β)}₁(n) mustconverge to

$\frac{1}{P - 1}$

in the presence of howling and the line coefficients may be computedusing:

$\begin{matrix}{{\overset{\hat{}}{\beta}(n)} = {{\left( {\begin{bmatrix}1 & 1 & \ldots & 1 \\1 & 2 & \ldots & P\end{bmatrix} \cdot \begin{bmatrix}1 & 1 \\1 & 2 \\\vdots & \vdots \\1 & P\end{bmatrix}} \right)^{- 1} \cdot \left\lbrack \begin{matrix}1 & 1 & \ldots & 1 \\1 & 2 & \ldots & P\end{matrix} \right\rbrack \cdot \left\lbrack \begin{matrix}{y^{\prime}\left( {n - P + 1} \right)} \\{y^{\prime}\left( {n - P + 2} \right)} \\\vdots \\{y^{\prime}(n)}\end{matrix} \right\rbrack} = {\overset{\sim}{X} \cdot \left\lbrack \begin{matrix}{y^{\prime}\left( {n - P + 1} \right)} \\{y^{\prime}\left( {n - P + 2} \right)} \\\vdots \\{y^{\prime}(n)}\end{matrix} \right\rbrack}}} & {{Eq}.\mspace{14mu}(6)}\end{matrix}$

Where {tilde over (x)} may be precomputed to avoid matrix inversion andto reduce the number of cycles. In addition, in order to reduce MIPS,y′(n) can be subsampled to only use a subset of the P samples to solvethe least squares problem presented in Eq. 3. In the example illustratedin FIG. 10 the new subset only includes 16 samples represented by eachof the circles and obtained from y(n), the samples being equidistantapart starting from first sample and ending with the last sample.

Smoothing may be applied to the estimated slope coefficient {circumflexover (β)}₁(n) and to the residual r(n)

r (n)=λ_(r) ·r (n−1)+(1−λ_(r))·r(n)   Eq. (7)

β ₁(n)=λ_(β)·β ₁(n−1)+(1−λ_(β))·{circumflex over (β)}₁(n)   Eq. (8)

In addition, according to one or more examples the parameter α is usedas a confidence interval on the estimated slope coefficient such thathowling is only determined to be detected if the slope is between theseconfidence intervals.

$\begin{matrix}{{\left( \frac{1}{P - 1} \right) \cdot \left( {1 - \alpha} \right)} \leq {\overset{\_}{\beta}}_{1} \leq {\left( \frac{1}{P - 1} \right) \cdot \left( {1 + \alpha} \right)}} & {{Eq}.\mspace{14mu}(9)}\end{matrix}$

According to one or more examples, the predetermined threshold of thelinearity metric is set such that a positive indication of howling isdetermined before log-linearity is actually reached. The positiveindication may therefore beneficially provide an advance indication thathowling is likely to occur or is imminent. Such examples may beimplemented in conjunction with a process which additionally monitorsthe change in the linearity metric over time. It will be appreciatedthat it is possible to detect when the linearity metric increases—i.e.when the logarithm of the energy of the signal u(n) tends towards, orapproaches, a certain degree of linearity. Thus, according to one ormore examples, once the linearity metric meets a predeterminedthreshold, a further detection process is carried out to ascertain ifthe linearity metric is tending towards or is approaching a value whichis indicates a high degree of linearity—i.e. that the linearity isincreasing. Specifically, a reduction in the value of the residuals r(n)over time will indicate that the system is tending towards howling.

According to one or more examples, the predetermined threshold of themeasure of linearity may be a maximum threshold. In other words, apositive indication of howling may be determined when the measure iflinearity falls below a predetermined threshold. In these examples, thepositive indication may still provide an advance indication that howlingis likely to occur or is imminent. Such examples may be implemented inconjunction with a process which monitors the rate of change in thelogarithm of the signal energy. It will be appreciated that when thisderivative is zero then the logarithm is perfectly linear, and so thepresence of howling may therefore be inferred by monitoring thederivative and determining when it is tending towards zero. In otherwords, a process may monitor Dy(n)=D log(u(n)), D being the general termused to denote the derivative, which may for example (with reference toFIGS. 7, 9 and 10) may be the derivative with respect to samples, andmay provide an advance indication that howling is likely to occur whenDy(n)≈0. In one example an advance indication that howling is likely tooccur may be provided when the derivative of the logarithm of the energyis below a threshold, in other words when Dy(n)<T, T representing athreshold that is approximately zero. In this way when the derivative ofthe logarithm of the energy is tending towards zero, as indicated by itbeing below the threshold T, this may provide an advance indication thathowling is likely to occur.

Thus, a further advantage of monitoring the linearity of the log-energyis that it is possible to observe when the system is tending towardshowling (as indicated e.g. by a goodness of fit measure). In otherwords, according to one or more examples, it is possible to observe whenthe system is starting to become unstable, rather than relying upon thedetection of a characteristic of howling after it has started. Thus,according to one or more examples, it is possible to determine whenhowling is likely, or imminent. This will be prior to the actualoccurrence of any audible howling.

The linearity metric determined by the howling detector 150 cantherefore be considered to be a representation or estimation of thestability of the noise control system. If the system is tending towardshowling, which is an unstable condition, adjustment may be beneficiallymade to the gain G of the circuit in order to restore the noise controlsystem to a more stable state. According to one or more examples, thegain is reduced by an amount which is related to a measure of the degreeof instability. Thus, according to one or more examples the gain may bereduced by an amount which depends on how unstable the system is. Thus,the howling detector may be configured to estimate a required amount ofgain reduction that is proportional to the degree of instability. Thismay, for example, be derived from the slope of the un-normalisedlog-energy. This slope, when howling is present, is proportional to thegain that the system needs to reduce to keep the system stable (nohowling).

Thus, the amount by which the gain needs to be adjusted can beconsidered to be a maximum stable gain (MSG) of the system. The maximumstable gain (MSG) can be considered to be a measure that shows if thesystem is stable (MSG negative) or if it is unstable (MSG positive).This is actually the minimum extra gain that the system needs todecrease to become stable.

The information from the slope can be also used to adjust the locationof poles and zeros of the feed-forward noise cancelling filter.

An advantage of determining an amount of gain adjustment required basedon the slope of the fitted trend line that it becomes possible to keepthe maximum gain in the ANC without making the system howl. Thus, somedegree of noise control can still take place even though the system isin or tending towards an unstable state. In contrast, if the gain isreduced by a fixed amount (35 dB), this noise control functionality iseffectively prevented.

FIG. 11 shows the log energy of the signal output for three differentcases (values of MSG) where the system becomes unstable after the sample48000. From this it is apparent that the slope of the fitted line isdirectly related to the desired MSG value. Thus, according to one ormore examples, it is possible to determine the minimum gain reductionnecessary.

FIG. 12 shows several plots relating to a test signal and signals usedby the proposed method to detect howling. The first plot shows the testsignal in the time domain over 3000 samples (approximately 62 ms). Thesecond plot displays the computed log energy from the signal shown firstplot. The third and fourth plots show the two metrics used to decide ifhowling is present. In this example, the system becomes unstable aroundsample 2300. At this point, the log energy y(n) starts to clearly growat a linear rate which makes the residual smaller since y(n) is closerto a line. Furthermore, the slope of this log energy y(n) (last figure)is also converging to the expected value plotted as a red line in thefigure.

FIG. 13 shows a process flow diagram to illustrate a process of howlingdetection according to one example. The process illustrated in FIG. 13may, for example, take place in a howling detector 150 as illustrated inFIGS. 5, 6 and 8.

In a first step ST1 a signal comprising audio data derived from amicrophone is obtained and forms an input signal u(n). In a second stepST2 the log energy y(n) of the signal u(n) is computed from theamplitude a(n) using, e.g. equation (2). The signal y(n) is normalisedat ST3 such that it is between 0 and 1, as given by equation 5. A lineof best fit is derived at ST4 in order to fit a straight line to thenormalised plot of the log energy. The resulting line or trend line issmoothed at ST5 using e.g. equation (9). Steps ST1 to ST5 inclusive canbe considered to form part of a processing stage A which comprises thegeneration of a trend line.

In a decision-making stage B, a first decision D1 is made at ST6 inorder to ascertain if the energy level y(n) is below a given threshold.If the energy level is below the threshold, a no-howling decision (orhowling flag=0) is reached. If the energy level is determined to be ator above the energy threshold, a number of further decision making stepsare carried out in order to determine a log-linearity metric.Specifically, at ST7 a goodness of fit measure of the computed trendline to the logarithm of the energy of the input signal over P samplesis determined. The goodness of fit measure may be determined, forexample, in accordance with equation (1) and the resulting linearitymetric may be smoothed according to equation (8).

A second decision D2 is made at ST9. Specifically, the linearity metricis compared with a predetermined threshold wherein, if the linearitymetric is below the predetermined threshold, indicating that a goodnessof fit between the observed samples and the trend line returns highresidual values, a no-howling decision is reached. However, if thelinearity metric is at or above the predetermined threshold, a thirddecision-making process D3 is performed at ST10 in order to ascertain ifthe slope of the estimated trend line falls between the confidenceintervals according to equation (11). If the slope does not fall betweenthe confidence intervals, a no-howling decision is reached. However, ifthe slope does fall between the confidence intervals, a result ofhowling (or howling flag=1) is reached.

It will be appreciated that the indication of howling will be reachedwhen the linearity metric exceeds a predetermined threshold and that thethreshold may be set to detect circumstances of imminent howling(tending towards howling) as well as circumstances of actual howling. Ifthe predetermined threshold is set to detect circumstances of imminenthowling (in other words the linearity metric exceeds a lower thresholdand/or may be tending towards a higher linearity metric threshold, thusindicating that the system becoming more unstable), the gain adjustermay be operable to adjust the gain by an amount proportional to thelevel of instability of the system, e.g. by a few decibels, rather thanby a larger (typically fixed) amount that will interrupt/kill the noisecontrol unit.

Thus, in a subsequent step a command may be issued to a gain adjustmentunit in order to adjust the gain of an associated noise control circuit.

The possible actions taken in response to the process illustrated inFIG. 13 may be as follows:

No howling: no action taken

Howling: Drop gain by fixed amount in order to interrupt ANC

Imminent howling: reduce ANC gain slightly (e.g. 3-5 dB or by an amountderived from the slope of the fitted line (without any normalisation).

It will be appreciated that a howling detection process according to oneor more examples may be restricted to at least one defined frequencyband—e.g. between f1 and f2. This may be achieved by filtering the inputsignal in order to eliminate certain frequencies or concentrate oncertain frequency band(s). An advantage of this arraignment is that“howling-type” acoustic patterns can be filtered out. That is, audiosounds such as whistling or sirens which may otherwise give rise to afalse positive indication of howling. Thus, an advantage of one or moreof the present examples is that the occurrence of false positives isalleviated, resulting in a more robust system.

According to one or more examples, and as illustrated in FIG. 14,howling detection may be carried out on the basis of a plurality offrequency bands. This may be referred to as multiband howling detection,wherein a plurality of filters 180 _(1 . . . M) allow the input signalu(n) to be tuned for a plurality M of frequency bands. Thus, the systemmay be considered to comprise a filterbank which carries out frequencyband splitting of the input signal.

The howling detector 150 _(1. . .M) is operable to determine alog-linearity metric for each frequency band. The slope and confidencemeasures may be readily combined in a combiner 190 by checking each bandindependently and then acting upon the channel with high confidence andslope closest to 1/(P−1). According to this arrangement, a decision ofhowling may be reached if howling is detected in any single frequencyband.

The skilled person will recognise that some aspects of theabove-described apparatus and methods may be embodied as processorcontrol code, for example on a non-volatile carrier medium such as adisk, CD- or DVD-ROM, programmed memory such as read only memory(Firmware), or on a data carrier such as an optical or electrical signalcarrier. For many applications examples of the invention will beimplemented on a DSP (Digital Signal Processor), ASIC (ApplicationSpecific Integrated Circuit) or FPGA (Field Programmable Gate Array).Thus the code may comprise conventional program code or microcode or,for example code for setting up or controlling an ASIC or FPGA. The codemay also comprise code for dynamically configuring re-configurableapparatus such as re-programmable logic gate arrays. Similarly the codemay comprise code for a hardware description language such as Verilog™or VHDL (Very high speed integrated circuit Hardware DescriptionLanguage). As the skilled person will appreciate, the code may bedistributed between a plurality of coupled components in communicationwith one another. Where appropriate, the examples may also beimplemented using code running on a field-(re)programmable analoguearray or similar device in order to configure analogue hardware.

Note that as used herein the term unit or module shall be used to referto a functional unit or block which may be implemented at least partlyby dedicated hardware components such as custom defined circuitry and/orat least partly be implemented by one or more software processors orappropriate code running on a suitable general purpose processor or thelike. A unit may itself comprise other units, modules or functionalunits. A unit may be provided by multiple components or sub-units whichneed not be co-located and could be provided on different integratedcircuits and/or running on different processors.

Examples may be implemented in a host device, especially a portableand/or battery powered host device such as a mobile computing device forexample a laptop or tablet computer, a games console, a remote controldevice, a home automation controller or a domestic appliance including asmart home device a domestic temperature or lighting control system, atoy, a machine such as a robot, an audio player, a video player, or amobile telephone for example a smartphone.

Examples of the disclosure may be provided according to any one of thefollowing numbered statements:

1. A processing module for a noise control circuit, the processingmodule comprising:

-   -   a howling detector configured to receive an input signal and to        determine a linearity metric based on the input signal, the        linearity metric comprising a measure of the linearity of a        logarithmic representation of the energy of the input signal;        and    -   a gain adjuster configured to adjust the gain of a noise control        unit.

2. The processing module of statement 1, wherein the linearity metric isderived by computing a difference between the logarithm of the energy ofthe signal and a trend line, the trend line being is a straight linerepresentation of the logarithm of the energy of the signal.

3. The processing module of statement 2, wherein coefficients of thetrend line are derived by a fitting a line to the logarithmicrepresentation of the energy of the input signal.

4. The processing module of statement 3, wherein fitting a line to thelogarithmic representation of the energy of the input signal comprisesperforming a least squares computation which minimises a differencebetween the trend line and the logarithmic representation of the energyof the input signal.

5. The processing module of any one of statements 2 to 4, wherein thelinearity metric is determined according to a goodness of fit measure ofthe computed trend line to the logarithm of the energy of the inputsignal over P samples.

6. The processing module of statement 5, wherein the goodness of fitmeasure r(n) is be represented by:

${r(n)} = \sqrt{\sum\limits_{i = 0}^{P - 1}\left( {{y\left( {n - i} \right)} - \left( {{{\hat{\beta}}_{0}(n)} + {\left( {P - i} \right) \cdot {{\hat{\beta}}_{1}(n)}}} \right)} \right)^{2}}$

where y(n) is the log energy of the input signal, {circumflex over(β)}₀(n) is the bias of the trend line and {circumflex over (β)}₁(n) isthe slope of the trend line.

7. The processing module of any preceding statement, wherein the howlingdetection unit is further configured to issue a command to the gainadjuster in order to reduce the gain if the linearity metric exceeds apredetermined threshold.

8. The processing module of any preceding statement, wherein the gainadjuster is configured to adjust the gain by a fixed amount.

9. The processing module of any preceding statement, wherein the howlingdetection unit is further configured to estimate a maximum stable gainof the noise control unit.

10. The processing module of statement 9 when appended directly orindirectly to statement 2, wherein the maximum stable gain isproportional to the slope of the trend line.

11. The processing module of any preceding statement, wherein thehowling detection unit is configured to determine, based on thedetermined linearity metric, if howling is likely or imminent.

12. An audio processing system comprising a processing module of anypreceding statement and further comprising a noise control unit forgenerating a noise control signal based on a reference input signalwhich represents a sound detected by a reference microphone.

13. The audio processing system of statement 12, wherein the processingmodule is connected to a speaker and wherein the speaker generates ananti-noise signal based on the noise control signal in order to cancelor at least reduce the noise detected by a reference microphone.

14. The audio processing system of statement 13, wherein the howlingdetector is provided in parallel with the noise control unit.

15. The audio processing system of any of statements 12-14, furthercomprising a filter configured to filter out one or more frequencies orfrequency bands of the input signal.

16. The audio processing system of any of statements 12-15, furthercomprising a filterbank configured to split the input signal into aplurality of frequency bands, wherein the howling detector is operableto determine a linearity metric for each frequency band.

17. A processing circuit for a noise control module comprising:

a gain adjustment mechanism configured to adjust the gain of a noisecontrol circuit if a plot of the energy of an input signal in the logdomain becomes linear or tends towards linearity.

18. The processing circuit of statement 17, wherein the gain of thenoise control circuit is adjusted by an amount that related to adifference between a measure of the linearity of the plot of the energyof the input signal in the log domain and a target linearity metric.

19. The processing module of any one of statements 1 to 15, in the formof a monolithic integrated circuit.

20. A device comprising a processing module according to any one ofstatements 1 to 15, or a processing circuit according to statement 17 or18, wherein the device comprises a mobile telephone, headphone, acousticnoise cancelling headphones, a smart watch, an audio player, a videoplayer, a mobile computing platform, a games device, a remote controllerdevice, a toy, a machine, or a home automation controller, a domesticappliance or other portable device.

21. A method of processing an audio signal comprising:

-   -   determining a linearity metric based on an input signal, the        linearity metric comprising a measure of the linearity of a        logarithmic representation of the energy of the input signal;        and    -   adjusting the gain of the noise control unit if the linearity        metric exceeds a predetermined threshold

22. The method of processing an audio signal of statement 21, whereinthe step of determining a linearity metric comprises computing adifference between the logarithm of the energy of the signal and a trendline, the trend line being is a straight line representation of thelogarithm of the energy of the signal.

It should be noted that the above-mentioned examples illustrate ratherthan limit the invention, and that those skilled in the art will be ableto design many alternative examples without departing from the scope ofthe appended claims. The word “comprising” does not exclude the presenceof elements or steps other than those listed in a claim, “a” or “an”does not exclude a plurality, and a single feature or other unit mayfulfil the functions of several units recited in the claims. Anyreference numerals or labels in the claims shall not be construed so asto limit their scope.

1.-20. (canceled)
 21. A howling detector configured to determine whetherhowling is likely or imminent on the basis of a parameter of a portionof an input signal.
 22. The howling detector of claim 21 wherein thehowling detector is configured to measure the parameter of the portionof the input signal.
 23. The howling detector of claim 21 wherein thehowling detector is configured to determine whether howling is likely orimminent on the basis of the linearity of the parameter of the portionof the input signal.
 24. The howling detector of claim 23 wherein thehowling detector is configured to determine the linearity of theparameter of the portion of the input signal.
 25. The howling detectorof claim 21 wherein the parameter is proportional to the energy of theportion of the input signal.
 26. The howling detector of claim 25wherein the parameter is proportional to the logarithm of the energy ofthe portion of the input signal.
 27. The howling detector of claim 26wherein the parameter of the portion of the input signal is proportionalto the difference between the logarithm of the energy of the portion ofthe input signal and a trend line, wherein the trend line comprises astraight line representation of the logarithm of the energy of theportion of the input signal.
 28. The howling detector of claim 27wherein the parameter of the portion of the input signal is proportionalto the residuals of the trend line.
 29. The howling detector of claim 27wherein the parameter of the portion of the input signal is proportionalto a goodness of fit measure of the trend line over P samples.
 30. Thehowling detector of claim 29 wherein the goodness of fit measure, r(n),is represented by:${r(n)} = {\sqrt{\sum\limits_{i = 0}^{P - 1}\left( {{y\left( {n - i} \right)} - \left( {{{\hat{\beta}}_{0}(n)} + {\left( {P - i} \right) \cdot {{\hat{\beta}}_{1}(n)}}} \right)} \right)^{2}}.}$where y(n) is the log energy of the input signal, {circumflex over(β)}₀(n) is the bias of the trend line and {circumflex over (β)}₁(n) isthe slope of the trend line.
 31. The howling detector of claim 27wherein the parameter is proportional to the slope of the trend line.32. The howling detector of claim 25 wherein the parameter isproportional to a derivative of the energy of the portion of the inputsignal.
 33. The howling detector of claim 26 wherein the parameter isproportional to a derivative of the logarithm of the energy of theportion of the input signal.
 34. A gain adjuster configured to adjustthe gain of a noise control circuit based on a parameter of a portion ofan input signal that indicates whether howling is likely or imminent.35. The gain adjuster of claim 34 wherein the gain adjuster isconfigured to adjust the gain of a noise control circuit based on thelinearity of the parameter of the portion of the input signal.
 36. Thegain adjuster of claim 34 wherein the parameter is proportional to theenergy of the portion of the input signal.
 37. The gain adjuster ofclaim 34 wherein the parameter is proportional to the logarithm of theenergy of the portion of the input signal.
 38. A howling detectorconfigured to measure a parameter of a portion of an input signal thatindicates whether howling is likely or imminent.
 39. A howling detectorconfigured to determine whether howling is likely or imminent on thebasis of the linearity of a parameter of a portion of an input signal.40. A gain adjuster configured to adjust the gain of a noise controlcircuit based on the linearity of a parameter of a portion of an inputsignal.